code: def predict(self, images, score_thresh=0.0):
      self.dataset.set_images(images)
      loader = create("TestReader")(self.dataset, 0)
  
      imid2path = self.dataset.get_imid2path()
      anno_file = self.dataset.get_anno()
      clsid2catid, catid2name = get_categories(
          self.cfg.metric, anno_file=anno_file
      )
  
      # Run Infer
      self.status["mode"] = "test"
      self.model.eval()
      if self.cfg.get("print_flops", False):
          flops_loader = create("TestReader")(self.dataset, 0)
          self._flops(flops_loader)
  
      results = []
      for step_id, data in enumerate(tqdm(loader)):
          self.status["step_id"] = step_id
          # forward
          outs = self.model(data)
          for key in ["im_shape", "scale_factor", "im_id"]:
              if isinstance(data, typing.Sequence):
                  outs[key] = data[0][key]
              else:
                  outs[key] = data[key]
          for key, value in outs.items():
              if hasattr(value, "numpy"):
                  outs[key] = value.numpy()
  
          infer_res = self.get_det_res(
              outs["bbox"],
              outs["bbox_num"],
              outs["im_id"],
              clsid2catid,
              catid2name,
              imid2path,
              score_thresh=score_thresh,
          )
          results.extend(infer_res)
  
      return results
  
file_path: PaddleSeg/contrib/QualityInspector/qinspector/det/engine/predictor.py
imports: import typing
  from tqdm import tqdm
  from ppdet.core.workspace import create
  from ppdet.data.source.category import get_categories
  from ppdet.engine import Trainer
  from qinspector.utils.logger import setup_logger
label_desc: Use image node_id 1 for calling the Predictor obj's underlying member method code with example usage: obj.predict(images, score_thresh) and returns: results